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Following Google’s lead

“I generally look at Google as a vast machine learning engine that’s been stuffed with data for a decade and a half. Everything that Google does is about reach for that underlying engine – reach to get data in and reach to surface it out. The legacy web search is just one expression of that, and so is the search advertising, and so are Gmail and Maps – they’re all built onto that underlying asset.
Hence, most of the experiments that Google has launched over the years are best seen as tests to see if they fit this model. Can you apply a vast expertise in understanding data, large numbers of computer scientists and data scientists, lots of infrastructure and a model of total automation and get something interesting and useful – can you get massive amounts of new data in, can you do something unique with it, and can you surface it back out? And, for all of these, are you solving hard, important problems with global scale?”
Benedict Evans in, “What does Google need on mobile“.

This is quite insightful stuff from Ben Evans (do check out the rest of his article too).

Does anyone see a parallel in the future of OSS to these same statements?

Let me pull out a few key points:

a vast machine learning engine

reach to get data in and reach to surface it out

all built onto that underlying asset

experiments… launched over the years are best seen as tests to see if they fit this model

Can you apply a vast expertise in understanding data, large numbers of computer scientists and data scientists, lots of infrastructure and a model of total automation and get something interesting and useful – can you get massive amounts of new data in, can you do something unique with it, and can you surface it back out?

are you solving hard, important problems with global scale?

At their heart, OSS are basically insight engines, as is Google search for that matter.

The interesting thing about OSS product development (and sales demonstrations), is that it tends to be based on the concept of adding and selling features. Generally, these features will revolve around gaining new insights, or helping the user develop insights in their own special way. But I see most OSS product features as pre-determined insight rules – rules that are locked in by the product developers.

A more powerful approach is to develop a platform that supports evolving, flexible insight generation through ongoing experimentation with the data. [As an aside, it’s from this mindset where tools like Splunk, Hadoop, etc have gained a foothold in many industries, not just the communications industry].

In many ways, it’s the data that’s more valuable than the OSS tools (although the tools become valuable for their ability to unlock insight from raw data). Data is the underlying asset that the tools are built upon. The great thing about data is that once collected, cleansed, augmented, etc, it becomes relatively easy to run experiments on.

This is where machine learning comes in. This is where product developers can spend more time with large amounts of real data (and customer processes) rather than small sets of mock data and test data. It’s where learnings are derived from global-scale data, not just each local customer. It’s where customers and vendors alike contribute in an ongoing manner to solving hard, important problems that effect them all.

These insight engines will need to deliver insights not just to customers (CSPs), but to their customers (subscribers).